<template>
  <div class="container mt-5">
    <div class="post">
      <header class="post-header">
        <div class="profile float-left">
          <img
            class="img-fluid z-depth-1 rounded-circle"
            src="../assets/img/me2.png"
          />
        </div>
        <h1 class="post-title" style="font-size: 28px">
          <span class="font-weight-bold">薛明亮</span>
        </h1>
        <p class="desc"></p>
        <article></article>
      </header>
      <div class="clearfix">
        <p>
          Senior Researcher, Microsoft Research Asia<br />
          Building 2, No. 5 Danling Street, Haidian District, Beijing, China<br />
          jindongwang [at] outlook.com, jindong.wang [at] microsoft.com<br />
          <a href="#" target="_blank" rel="noopener noreferrer"
            >Google scholar</a
          >
          |
          <a href="#" target="_blank" rel="noopener noreferrer">DBLP</a>
          |
          <a href="#" target="_blank" rel="noopener noreferrer">Github</a>
          ||
          <a href="#" target="_blank" rel="noopener noreferrer">Twitter</a>
          |
          <a href="#" target="_blank" rel="noopener noreferrer">Zhihu</a>
          |
          <a href="#" target="_blank" rel="noopener noreferrer">Wechat</a>
          |
          <a href="#" target="_blank" rel="noopener noreferrer">Bilibili</a>
          ||
          <a href="#" target="_blank" rel="noopener noreferrer">CV</a>
          <a href="#" target="_blank" rel="noopener noreferrer">CV (Chinese)</a>
        </p>
        <p>
          I’m currently a Senior Researcher at
          <a href="#" target="_blank" rel="noopener noreferrer"
            >Microsoft Research Asia (MSRA)</a
          >
          , in a group managed by
          <a href="#" target="_blank" rel="noopener noreferrer">Xing Xie</a>
          . Before joining MSRA, I obtained my Ph.D. from Institute of Computing
          Technology, Chinese Academy of Sciences in June, 2019. My doctoral
          thesis was awarded the excellent Ph.D. thesis of Chinese Academy of
          Sciences. In 2018/04–2018/08, I was a visitor of Prof.
          <a href="#" target="_blank" rel="noopener noreferrer">Qiang Yang</a>
          ’s group at Hong Kong University of Science and Technology (HKUST). My
          work on transfer learning won the best paper awards in ICCSE 2018 and
          FTL-IJCAI 2019. In 2021, I published the textbook
          <a href="#" target="_blank" rel="noopener noreferrer"
            >Introduction to Transfer Learning</a
          >
          , a hands-on introduction to transfer learning. In 2022, I was
          selected as one of the
          <a href="#" target="_blank" rel="noopener noreferrer"
            >2022 AI 2000 Most Influential Scholars</a
          >
          by AMiner between 2012-2021 (ranked 49/2000). Four of my first-author
          papers are ranked by Google Scholar as
          <a href="#" target="_blank" rel="noopener noreferrer"
            >highly-cited papers</a
          >
          . I gave tutorials at
          <a href="#" target="_blank" rel="noopener noreferrer">IJCAI’22</a>.
        </p>
        <p>
          <strong>Research interest:</strong> robust machine learning,
          out-of-distribution / domain generalization, transfer learning,
          semi-supervised learning, federated learning, and related applications
          such as activity recognition and computer vision. These days, I’m
          particularly interested in Large Language Models (LLMs) robustness.
          See this
          <a href="#" target="_blank" rel="noopener noreferrer">page</a>
          for more details.
          <em
            >Interested in
            <a href="#" target="_blank" rel="noopener noreferrer">internship</a>
            or collaboration? Contact me.</em
          >
        </p>
        <p>
          <strong>Announcement:</strong>
          I’m experimenting a new form of research collaboration. You can click
          <a href="#" target="_blank" rel="noopener noreferrer">here</a>
          if you are interested!
        </p>
      </div>
      <div class="news">
        <!--------------------------- News--------------------------------------------- -->
        <h4>News</h4>
        <div class="table-responsive">
          <table class="table table-sm table-borderless">
            <tr>
              <th scope="row">May 17, 2023</th>
              <td>Two papers are accepted by KDD 2023.</td>
            </tr>
            <tr>
              <th scope="row">May 4, 2023</th>
              <td>
                The large model for large model evaluation “PandaLM” is released
                on Github! [<a
                  href="#"
                  target="_blank"
                  rel="noopener noreferrer"
                  >PandaLM</a
                >]
              </td>
            </tr>
            <tr>
              <th scope="row">May 3, 2023</th>
              <td>
                Our paper “GLUE-X: Evaluating Natural Language Understanding
                Models from an Out-of-distribution Generalization Perspective”
                is accepted by ACL 2023 findings! [<a
                  href="#"
                  target="_blank"
                  rel="noopener noreferrer"
                  >paper</a
                >]
              </td>
            </tr>
            <tr>
              <th scope="row">May 1, 2023</th>
              <td>
                We will give a tutorial on KDD 2023 named “Trustworthy machine
                learning: generalization, robustness, and interpretability”!
              </td>
            </tr>
            <tr>
              <th scope="row">Apr 17, 2023</th>
              <td>
                Two papers (FedCLIP and ChatGPT robustness) are accepted by ICLR
                2023 workshop on robust large models! [<a
                  href="#"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Papers</a
                >]
              </td>
            </tr>
            <tr>
              <th scope="row">Apr 5, 2023</th>
              <td>
                The English version of 迁移学习导论 is published online: [<a
                  href="#"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Springer</a
                >]
              </td>
            </tr>
          </table>
        </div>
      </div>
      <!-- ------------------------------Highlights------------------------------------ -->
      <h4 id="highlights">Highlights</h4>
      <ol>
        <li>
          Four of my papers are highly cited and ranked top 20 globally in
          recent 5 years in Google scholar metrics. See
          <a
            href="#"
            target="_blank"
            rel="noopener noreferrer noopener noreferrer"
            >here</a
          >.
        </li>
        <li>
          I wrote a popular book
          <a
            href="#"
            target="_blank"
            rel="noopener noreferrer noopener noreferrer"
            >Introduction to Transfer Learning</a
          >
          to make it easy to learn, understand, and use transfer learning.
        </li>
        <li>
          I lead the most popular transfer learning and semi-supervised learning
          projects on Github:
          <a
            href="#"
            target="_blank"
            rel="noopener noreferrer noopener noreferrer"
            >Transfer learning repo</a
          >,
          <a
            href="#"
            target="_blank"
            rel="noopener noreferrer noopener noreferrer"
            >Semi-supervised learning repo</a
          >, and
          <a
            href="#"
            target="_blank"
            rel="noopener noreferrer noopener noreferrer"
            >Personalized federated learning repo</a
          >.
        </li>
        <li>
          I was selected into the list of
          <a href="#" target="_blank" rel="noopener noreferrer"
            >2022 AI 2000 Most Influential Scholars</a
          >
          by AMiner in recognition of my contributions in the field of
          multimedia between 2012-2021 (ranked 49/2000)
        </li>
      </ol>
      <!-- ------------------------Selected publications------------------------------- -->
      <div class="publications">
        <h4>Selected publications</h4>
        <ol class="bibliography">
          <li>
            <div id="zhang2023domain" class="col-sm-8" style="max-width: 100%">
              <div
                class="title"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Domain-Specific Risk Minimization for Out-of-Distribution
                Generalization
              </div>
              <div
                class="author"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Yi-Fan Zhang,
                <em><b>Jindong Wang</b></em
                ><sup>#</sup>
                ,Jian Liang, Zhang Zhang, Baosheng Yu, Liang Wang, Dacheng Tao,
                and Xing Xie
              </div>
              <div
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: -0.1em;
                "
              >
                <em
                  >The 29th ACM SIGKDD Conference on Knowledge Discovery and
                  Data Mining (KDD)</em
                >
                2023 | [
                <a
                  href="#"
                  role="button"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >arXiv</a
                >
                <!--  -->
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Code</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Zhihu</a
                >
                ]
              </div>
            </div>
          </li>
          <li>
            <div
              id="lu2023generalized"
              class="col-sm-8"
              style="max-width: 100%"
            >
              <div
                class="title"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Out-of-distribution Representation Learning for Time Series
                Classification
              </div>
              <div
                class="author"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Wang Lu,
                <em><b>Jindong Wang</b></em
                ><sup>#</sup>
                , Xinwei Sun, Yiqiang Chen, and Xing Xie
              </div>
              <div
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: -0.1em;
                "
              >
                <em
                  >International Conference on Learning Representations
                  (ICLR)</em
                >
                2023 | [
                <a
                  href="#"
                  role="button"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >arXiv</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Code</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Website</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Zhihu</a
                >
                ]
              </div>
            </div>
          </li>
          <li>
            <div
              id="wang2023freematch"
              class="col-sm-8"
              style="max-width: 100%"
            >
              <div
                class="title"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                FreeMatch: Self-adaptive Thresholding for Semi-supervised
                Learning
              </div>
              <div
                class="author"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, Zhen Wu,
                <em><b>Jindong Wang</b></em
                ><sup>#</sup>
                , Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, Bernt
                Schiele, and Xing Xie
              </div>
              <div
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: -0.1em;
                "
              >
                <em
                  >International Conference on Learning Representations
                  (ICLR)</em
                >
                2023 | [
                <a
                  href="#"
                  role="button"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >arXiv</a
                >
                <!--  -->
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Code</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Website</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Zhihu</a
                >
                ]
              </div>
            </div>
          </li>
          <li>
            <div
              id="wang2022generalizing"
              class="col-sm-8"
              style="max-width: 100%"
            >
              <div
                class="title"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Generalizing to Unseen Domains: A Survey on Domain
                Generalization
              </div>
              <div
                class="author"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                <em><b>Jindong Wang</b></em>
                , Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu,
                Yiqiang Chen, Wenjun Zeng, and Philip S. Yu
              </div>
              <div
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: -0.1em;
                "
              >
                <em
                  >IEEE Transactions on Knowledge and Data Engineering
                  (TKDE)</em
                >
                2022 | [
                <a
                  href="#"
                  role="button"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >arXiv</a
                >
                <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                  >PDF</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Code</a
                >
                <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                  >Slides</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Website</a
                >
                ]
              </div>
              <div class="bibtex hidden">
                <figure class="highlight">
                  <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                        class="p">{</span><span class="nl">wang2022generalizing</span><span class="p">,</span>
                      <span class="na">title</span> <span class="p">=</span> <span class="s">{Generalizing to Unseen
                        Domains: A Survey on Domain Generalization}</span><span class="p">,</span>
                      <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and Lan,
                        Cuiling and Liu, Chang and Ouyang, Yidong and Qin, Tao and Lu, Wang and Chen, Yiqiang and Zeng,
                        Wenjun and Yu, Philip S.}</span><span class="p">,</span>
                      <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Transactions on
                        Knowledge and Data Engineering (TKDE)}</span><span class="p">,</span>
                      <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                        class="p">,</span>
                      <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TKDE}</span><span
                        class="p">,</span>
                      <span class="na">arxiv</span> <span class="p">=</span> <span
                        class="s">{https://arxiv.org/abs/2103.03097}</span><span class="p">,</span>
                      <span class="na">code</span> <span class="p">=</span> <span
                        class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG}</span><span
                        class="p">,</span>
                      <span class="na">slides</span> <span class="p">=</span> <span
                        class="s">{DGtutorial_ijcai22.pdf}</span><span class="p">,</span>
                      <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">pdf</span> <span class="p">=</span> <span
                        class="s">{DG_survey_TKDE22.pdf}</span><span class="p">,</span>
                      <span class="na">website</span> <span class="p">=</span> <span
                        class="s">{https://dgresearch.github.io/}</span>
                      <span class="p">}</span></code></pre>
                </figure>
              </div>
            </div>
          </li>
          <li>
            <div id="lu2022semantic" class="col-sm-8" style="max-width: 100%">
              <div
                class="title"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Semantic-Discriminative Mixup for Generalizable Sensor-based
                Cross-domain Activity Recognition
              </div>
              <div
                class="author"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Wang Lu,
                <em><b>Jindong Wang</b></em
                ><sup>#</sup>
                , Yiqiang Chen, Sinno Pan, Chunyu Hu, and Xin Qin
              </div>
              <div
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: -0.1em;
                "
              >
                <em
                  >Proceedings of the ACM on Interactive, Mobile, Wearable, and
                  Ubiquitous Technologies (IMWUT, i.e., UbiComp)</em
                >
                2022 | [
                <a
                  href="#"
                  role="button"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >arXiv</a
                >
                <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                  >PDF</a
                >
                ]
              </div>
              <div class="bibtex hidden">
                <figure class="highlight">
                  <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                        class="p">{</span><span class="nl">lu2022semantic</span><span class="p">,</span>
                      <span class="na">title</span> <span class="p">=</span> <span class="s">{Semantic-Discriminative
                        Mixup for Generalizable Sensor-based Cross-domain Activity Recognition}</span><span
                        class="p">,</span>
                      <span class="na">author</span> <span class="p">=</span> <span class="s">{Lu, Wang and Wang,
                        Jindong and Chen, Yiqiang and Pan, Sinno and Hu, Chunyu and Qin, Xin}</span><span
                        class="p">,</span>
                      <span class="na">journal</span> <span class="p">=</span> <span class="s">{Proceedings of the ACM
                        on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT, i.e.,
                        UbiComp)}</span><span class="p">,</span>
                      <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                        class="p">,</span>
                      <span class="na">abbr</span> <span class="p">=</span> <span class="s">{IMWUT}</span><span
                        class="p">,</span>
                      <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">arxiv</span> <span class="p">=</span> <span
                        class="s">{http://arxiv.org/abs/2206.06629}</span><span class="p">,</span>
                      <span class="na">pdf</span> <span class="p">=</span> <span class="s">{imwut22-sdmix.pdf}</span>
                      <span class="p">}</span></code></pre>
                </figure>
              </div>
            </div>
          </li>
          <li>
            <div
              id="zhang2022adaptive"
              class="col-sm-8"
              style="max-width: 100%"
            >
              <div
                class="title"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Adaptive Memory Networks with Self-supervised Learning for
                Unsupervised Anomaly Detection
              </div>
              <div
                class="author"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Yuxin Zhang,
                <em><b>Jindong Wang</b></em
                ><sup>#</sup>
                , Yiqiang Chen, Han Yu, and Tao Qin
              </div>
              <div
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: -0.1em;
                "
              >
                <em
                  >IEEE Transactions on Knowledge and Data Engineering
                  (TKDE)</em
                >
                2022 | [
                <a
                  href="#"
                  role="button"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >arXiv</a
                >
                <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                  >PDF</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Code</a
                >
                ]
              </div>
              <div class="bibtex hidden">
                <figure class="highlight">
                  <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                        class="p">{</span><span class="nl">zhang2022adaptive</span><span class="p">,</span>
                      <span class="na">title</span> <span class="p">=</span> <span class="s">{Adaptive Memory Networks
                        with Self-supervised Learning for Unsupervised Anomaly Detection}</span><span class="p">,</span>
                      <span class="na">author</span> <span class="p">=</span> <span class="s">{Zhang, Yuxin and Wang,
                        Jindong and Chen, Yiqiang and Yu, Han and Qin, Tao}</span><span class="p">,</span>
                      <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Transactions on
                        Knowledge and Data Engineering (TKDE)}</span><span class="p">,</span>
                      <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                        class="p">,</span>
                      <span class="na">abbr</span> <span class="p">=</span> <span class="s">{TKDE}</span><span
                        class="p">,</span>
                      <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">arxiv</span> <span class="p">=</span> <span
                        class="s">{https://arxiv.org/abs/2201.00464}</span><span class="p">,</span>
                      <span class="na">pdf</span> <span class="p">=</span> <span class="s">{tkde22_amsl.pdf}</span><span
                        class="p">,</span>
                      <span class="na">code</span> <span class="p">=</span> <span
                        class="s">{https://github.com/zhangyuxin621/AMSL}</span>
                      <span class="p">}</span></code></pre>
                </figure>
              </div>
            </div>
          </li>
          <li>
            <div id="zhang2022remos" class="col-sm-8" style="max-width: 100%">
              <div
                class="title"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                ReMoS: Reducing Defect Inheritance in Transfer Learning via
                Relevant Model Slicing
              </div>
              <div
                class="author"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Ziqi Zhang, Yuanchun Li,
                <em><b>Jindong Wang</b></em>
                , Bingyan Liu, Ding Li, Xiangqun Chen, Yao Guo, and Yunxin Liu
              </div>
              <div
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: -0.1em;
                "
              >
                <em
                  >44th International Conference on Software Engineering
                  (ICSE)</em
                >
                2022 | [
                <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                  >PDF</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Code</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Video</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Zhihu</a
                >
                ]
              </div>
              <div class="bibtex hidden">
                <figure class="highlight">
                  <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                        class="p">{</span><span class="nl">zhang2022remos</span><span class="p">,</span>
                      <span class="na">title</span> <span class="p">=</span> <span class="s">{ReMoS: Reducing Defect
                        Inheritance in Transfer Learning via Relevant Model Slicing}</span><span class="p">,</span>
                      <span class="na">author</span> <span class="p">=</span> <span class="s">{Zhang, Ziqi and Li,
                        Yuanchun and Wang, Jindong and Liu, Bingyan and Li, Ding and Chen, Xiangqun and Guo, Yao and
                        Liu, Yunxin}</span><span class="p">,</span>
                      <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{44th International
                        Conference on Software Engineering (ICSE)}</span><span class="p">,</span>
                      <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span
                        class="p">,</span>
                      <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ICSE}</span><span
                        class="p">,</span>
                      <span class="na">pdf</span> <span class="p">=</span> <span
                        class="s">{icse22-remos.pdf}</span><span class="p">,</span>
                      <span class="na">code</span> <span class="p">=</span> <span
                        class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/deep/ReMoS}</span><span
                        class="p">,</span>
                      <span class="na">zhihu</span> <span class="p">=</span> <span
                        class="s">{https://zhuanlan.zhihu.com/p/446453487}</span><span class="p">,</span>
                      <span class="na">video</span> <span class="p">=</span> <span
                        class="s">{https://www.bilibili.com/video/BV1mi4y1C7bP}</span><span class="p">,</span>
                      <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span>
                      <span class="p">}</span></code></pre>
                </figure>
              </div>
            </div>
          </li>
          <li>
            <div
              id="zhang2021flexmatch"
              class="col-sm-8"
              style="max-width: 100%"
            >
              <div
                class="title"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Flexmatch: Boosting semi-supervised learning with curriculum
                pseudo labeling
              </div>
              <div
                class="author"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu,
                <em><b>Jindong Wang</b></em
                ><sup>#</sup>
                , Manabu Okumura, and Takahiro Shinozaki
              </div>
              <div
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: -0.1em;
                "
              >
                <em
                  >Advances in Neural Information Processing Systems
                  (NeurIPS)</em
                >
                2021 | [
                <a
                  href="#"
                  role="button"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >arXiv</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >PDF</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Code</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Slides</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Video</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Zhihu</a
                >
                ]
              </div>
              <div class="bibtex hidden">
                <figure class="highlight">
                  <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span
                        class="p">{</span><span class="nl">zhang2021flexmatch</span><span class="p">,</span>
                      <span class="na">title</span> <span class="p">=</span> <span class="s">{Flexmatch: Boosting
                        semi-supervised learning with curriculum pseudo labeling}</span><span class="p">,</span>
                      <span class="na">author</span> <span class="p">=</span> <span class="s">{Zhang, Bowen and Wang,
                        Yidong and Hou, Wenxin and Wu, Hao and Wang, Jindong and Okumura, Manabu and Shinozaki,
                        Takahiro}</span><span class="p">,</span>
                      <span class="na">journal</span> <span class="p">=</span> <span class="s">{Advances in Neural
                        Information Processing Systems (NeurIPS)}</span><span class="p">,</span>
                      <span class="na">volume</span> <span class="p">=</span> <span class="s">{34}</span><span
                        class="p">,</span>
                      <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span
                        class="p">,</span>
                      <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">abbr</span> <span class="p">=</span> <span class="s">{NeurIPS}</span><span
                        class="p">,</span>
                      <span class="na">arxiv</span> <span class="p">=</span> <span
                        class="s">{https://arxiv.org/abs/2110.08263}</span><span class="p">,</span>
                      <span class="na">pdf</span> <span class="p">=</span> <span
                        class="s">{http://jd92.wangassets/files/flexmatch_nips21.pdf}</span><span class="p">,</span>
                      <span class="na">code</span> <span class="p">=</span> <span
                        class="s">{https://github.com/TorchSSL/TorchSSL}</span><span class="p">,</span>
                      <span class="na">zhihu</span> <span class="p">=</span> <span
                        class="s">{https://zhuanlan.zhihu.com/p/422930830}</span><span class="p">,</span>
                      <span class="na">video</span> <span class="p">=</span> <span
                        class="s">{https://www.youtube.com/watch?v=aYuUwyZl_WY}</span><span class="p">,</span>
                      <span class="na">slides</span> <span class="p">=</span> <span
                        class="s">{https://www.jianguoyun.com/p/DXeFVg8QjKnsBRibj54E}</span><span class="p">,</span>
                      <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span>
                      <span class="p">}</span></code></pre>
                </figure>
              </div>
            </div>
          </li>
          <li>
            <div id="du2021adarnn" class="col-sm-8" style="max-width: 100%">
              <div
                class="title"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Adarnn: Adaptive learning and forecasting of time series
              </div>
              <div
                class="author"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Yuntao Du,
                <em><b>Jindong Wang</b></em
                ><sup>#</sup>
                , Wenjie Feng, Sinno Pan, Tao Qin, Renjun Xu, and Chongjun Wang
              </div>
              <div
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: -0.1em;
                "
              >
                <em
                  >The 30th ACM International Conference on Information &amp;
                  Knowledge Management (CIKM)</em
                >
                2021 | [
                <a
                  href="#"
                  role="button"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >arXiv</a
                >
                <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                  >PDF</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Code</a
                >
                ]
              </div>
              <div class="bibtex hidden">
                <figure class="highlight">
                  <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                        class="p">{</span><span class="nl">du2021adarnn</span><span class="p">,</span>
                      <span class="na">title</span> <span class="p">=</span> <span class="s">{Adarnn: Adaptive learning
                        and forecasting of time series}</span><span class="p">,</span>
                      <span class="na">author</span> <span class="p">=</span> <span class="s">{Du, Yuntao and Wang,
                        Jindong and Feng, Wenjie and Pan, Sinno and Qin, Tao and Xu, Renjun and Wang,
                        Chongjun}</span><span class="p">,</span>
                      <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{The 30th ACM
                        International Conference on Information \&amp; Knowledge Management (CIKM)}</span><span
                        class="p">,</span>
                      <span class="na">pages</span> <span class="p">=</span> <span class="s">{402--411}</span><span
                        class="p">,</span>
                      <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span
                        class="p">,</span>
                      <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">abbr</span> <span class="p">=</span> <span class="s">{CIKM}</span><span
                        class="p">,</span>
                      <span class="na">corr</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">arxiv</span> <span class="p">=</span> <span
                        class="s">{https://arxiv.org/abs/2108.04443}</span><span class="p">,</span>
                      <span class="na">code</span> <span class="p">=</span> <span
                        class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/deep/adarnn}</span><span
                        class="p">,</span>
                      <span class="na">pdf</span> <span class="p">=</span> <span class="s">{cikm21-adarnn.pdf}</span>
                      <span class="p">}</span></code></pre>
                </figure>
              </div>
            </div>
          </li>
          <li>
            <div id="wang2018visual" class="col-sm-8" style="max-width: 100%">
              <div
                class="title"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Visual domain adaptation with manifold embedded distribution
                alignment
              </div>
              <div
                class="author"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                <em><b>Jindong Wang</b></em>
                , Wenjie Feng, Yiqiang Chen, Han Yu, Meiyu Huan, and Philip S Yu
              </div>
              <div
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: -0.1em;
                "
              >
                <em>The 26th ACM international conference on Multimedia</em>
                2018 | [
                <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                  >PDF</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Supp</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Code</a
                >
                <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                  >Poster</a
                >
                ]
              </div>
              <div><b>(400+ citations; 2nd most cited paper in MM’18)</b></div>
              <div class="bibtex hidden">
                <figure class="highlight">
                  <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                        class="p">{</span><span class="nl">wang2018visual</span><span class="p">,</span>
                      <span class="na">title</span> <span class="p">=</span> <span class="s">{Visual domain adaptation
                        with manifold embedded distribution alignment}</span><span class="p">,</span>
                      <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and Feng,
                        Wenjie and Chen, Yiqiang and Yu, Han and Huang, Meiyu and Yu, Philip S}</span><span
                        class="p">,</span>
                      <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{The 26th ACM
                        international conference on Multimedia}</span><span class="p">,</span>
                      <span class="na">pages</span> <span class="p">=</span> <span class="s">{402--410}</span><span
                        class="p">,</span>
                      <span class="na">year</span> <span class="p">=</span> <span class="s">{2018}</span><span
                        class="p">,</span>
                      <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ACMMM}</span><span
                        class="p">,</span>
                      <span class="na">code</span> <span class="p">=</span> <span
                        class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/traditional/MEDA}</span><span
                        class="p">,</span>
                      <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a11_mm18.pdf}</span><span
                        class="p">,</span>
                      <span class="na">supp</span> <span class="p">=</span> <span
                        class="s">{https://www.jianguoyun.com/p/DRuWOFkQjKnsBRjkr2E}</span><span class="p">,</span>
                      <span class="na">poster</span> <span class="p">=</span> <span
                        class="s">{poster_mm18.pdf}</span><span class="p">,</span>
                      <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">special</span> <span class="p">=</span> <span class="s">{400+ citations; 2nd most
                        cited paper in MM'18}</span>
                      <span class="p">}</span></code></pre>
                </figure>
              </div>
            </div>
          </li>
          <li>
            <div id="wang2017balanced" class="col-sm-8" style="max-width: 100%">
              <div
                class="title"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                Balanced distribution adaptation for transfer learning
              </div>
              <div
                class="author"
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: 0em;
                "
              >
                <em><b>Jindong Wang</b></em>
                , Yiqiang Chen, Shuji Hao, Wenjie Feng, and Zhiqi Shen
              </div>
              <div
                style="
                  padding-top: 0em;
                  padding-bottom: 0px;
                  margin-bottom: -0.1em;
                "
              >
                <em>IEEE international conference on data mining (ICDM)</em>
                2017 | [
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >HTML</a
                >
                <a href="#" style="padding-top: 0em; padding-bottom: 0px"
                  >PDF</a
                >
                <a
                  href="#"
                  style="padding-top: 0em; padding-bottom: 0px"
                  target="_blank"
                  rel="noopener noreferrer"
                  >Code</a
                >
                ]
              </div>
              <div><b>(400+ citations; most cited paper in ICDM’17)</b></div>
              <div class="bibtex hidden">
                <figure class="highlight">
                  <pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span
                        class="p">{</span><span class="nl">wang2017balanced</span><span class="p">,</span>
                      <span class="na">title</span> <span class="p">=</span> <span class="s">{Balanced distribution
                        adaptation for transfer learning}</span><span class="p">,</span>
                      <span class="na">author</span> <span class="p">=</span> <span class="s">{Wang, Jindong and Chen,
                        Yiqiang and Hao, Shuji and Feng, Wenjie and Shen, Zhiqi}</span><span class="p">,</span>
                      <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE international
                        conference on data mining (ICDM)}</span><span class="p">,</span>
                      <span class="na">pages</span> <span class="p">=</span> <span class="s">{1129--1134}</span><span
                        class="p">,</span>
                      <span class="na">year</span> <span class="p">=</span> <span class="s">{2017}</span><span
                        class="p">,</span>
                      <span class="na">organization</span> <span class="p">=</span> <span class="s">{IEEE}</span><span
                        class="p">,</span>
                      <span class="na">bibtex_show</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">abbr</span> <span class="p">=</span> <span class="s">{ICDM}</span><span
                        class="p">,</span>
                      <span class="na">code</span> <span class="p">=</span> <span
                        class="s">{https://github.com/jindongwang/transferlearning/tree/master/code/BDA}</span><span
                        class="p">,</span>
                      <span class="na">pdf</span> <span class="p">=</span> <span class="s">{a08_icdm17.pdf}</span><span
                        class="p">,</span>
                      <span class="na">html</span> <span class="p">=</span> <span
                        class="s">{http://ieeexplore.ieee.org/document/8215613/?part=1}</span><span class="p">,</span>
                      <span class="na">selected</span> <span class="p">=</span> <span class="s">{true}</span><span
                        class="p">,</span>
                      <span class="na">special</span> <span class="p">=</span> <span class="s">{400+ citations; most
                        cited paper in ICDM'17}</span>
                      <span class="p">}</span></code></pre>
                </figure>
              </div>
            </div>
          </li>
        </ol>
      </div>
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